Difference between the roles of an AI Engineer and an ML Engineer

AI Engineer Certification - USAII
November 25, 2023

In modern times, AI Engineers and ML Engineers are among the most popular job roles that have been driving transformation across industries. Artificial Intelligence and Machine Learning technology are often related but distinct, and so are the roles of AI and ML engineers. Often both these roles are referred to interchangeably, but over the years they have emerged, each carrying its own sets of responsibilities and skill requirements.

In this article, we will explore in detail the difference between the roles of an AI Engineer and an ML Engineer and shed light on what contributions each of these roles make in the world of technology.


AI Engineer: Architects of Intelligence Systems

Artificial Intelligence Engineers are popularly referred to as the architects behind the development and deployment of artificial intelligence systems. AI Engineers design solutions that help machines assist human intelligence and abilities. AI engineering is a subset of computer science and AI Engineers are dedicated to building intelligence systems that can mimic human work without being explicitly programmed.

ML Engineer: Craft Intelligence Algorithms

AI Engineers make AI systems, whereas Machine Learning Engineers focus on the development and deployment of machine learning models and algorithms.

Machine Learning is a subset of artificial intelligence that is dedicated to making intelligence systems that are capable of making decisions based on the data sets on which they are trained.

ML engineers design and develop ML models that can analyze vast amounts of data and can help automate making decisions.

Key Differences between AI Engineers and ML Engineers

Key DifferencesAI EngineersML Engineers
Scope of workWork on a broader set of tasks involving various forms of machine intelligence, such as neural networks. Develop AI models for diverse applications beyond machine learning.Focus more on ML algorithms and models, specializing in self-tuning capabilities to learn and make predictions from large datasets.
Tool sets usedRequire knowledge of basic programming languages like Python. Use various AI libraries and frameworks. May delve less deeply into specific ML libraries.Require knowledge of basic programming languages like Python. Use various AI libraries and frameworks. May delve less deeply into specific ML libraries.              
ApplicationInvolved in applications ranging from robotics to natural language processing and beyond. Address a wide spectrum of AI applications beyond data-driven tasks.Concentrate on data-driven applications, such as predictive analytics or classification tasks. Specialize in developing models that learn patterns from data.
Depth of data scienceHave a solid understanding of data science but may not engage as closely with data preprocessing, feature engineering, and model evaluation. Focus more on the overall architecture of intelligent systems.Work closely with data, involving tasks like data preprocessing, feature engineering, and model evaluation. Have a hands-on approach to the intricacies of working with large datasets.

Roles and Responsibilities

AI Engineer

  • Develop algorithms: AI Engineers develop algorithms that help machines learn from data so that they can make decisions and improve their performance in the long run.
  • Natural Language Processing (NLP): By using this AI skill, AI engineers can build models that can understand, interpret, and generate human-like language.
  • Computer Vision: AI Engineers are also responsible for using computer vision technology to help machines interpret and understand visual information. Image recognition, object detection, facial recognition, etc. come under computer vision tasks.
  • Robotics: Though robotics engineering is an entirely different role, AI engineers in many organizations are required to work on robotics and contribute to the development of intelligent machines that can interact with the physical world as humans do.

Skills required to become an AI Engineer

There are some specific skill sets that distinguish AI engineers from ML engineers such as:

  • Understanding of machine learning libraries like TensorFlow and PyTorch
  • Understanding of specific domains where the AI systems will operate
  • Ability to handle complex problems while designing robust AI Systems
  • Good communication skills and proficiency in programming languages

While soft skills can be gained with training and experience over time, the core AI skills can be learned with the help of top artificial intelligence certification programs.

ML Engineer

  • A major role of ML Engineers includes cleaning, transforming, and preparing data for training machine learning models. So they need to check for missing values, look after scaling features, and ensure the dataset is suitable for training.
  • ML Engineers must select the right machine learning algorithms and training models for the labeled datasets. They also fine-tune parameters and optimize model performance.
  • They also have to look after feature engineering, a process in which relevant features are selected to enhance the performance of machine learning models.
  • Monitor the performance of deployed models and make necessary changes whenever required to maintain its accuracy over time.

Skills Required for ML Engineers

ML Engineers possess many different skill sets than AI Engineers including:

  • Understanding of statistical concepts
  • Proficiency in programming languages like R, and familiarity with machine learning libraries
  • Ability to work with large datasets and apply data preprocessing techniques
  • Solid understanding of machine learning algorithms and their applications to different types of problems

Learn AI and ML Skills with CAIE™

The United States Artificial Intelligence Institute (USAII®) offers one of the most popular credible AI and ML certifications for beginners – the Certified Artificial Intelligence Engineer (CAIE™).

This program has been designed for AI and ML engineers and covers the fundamental concepts of AI and ML engineering. By earning this certification, students can build a strong understanding of AI and ML concepts and gain the AI skills required to advance in this domain.

The Artificial Intelligence and machine learning industry is growing at a rapid pace leading to an increase in the number of AI jobs and the demand for AI engineers and ML engineers will surge in 2024 and beyond. So, take advantage of this AI and ML certification program and get started with your AI career now.

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